The present invention relates to hyper-spectral imaging, in general, generating and collecting hyper-spectral images, and, processing and analyzing hyper-spectral image data and information, in particular. More particularly, the present invention relates to a method of processing and analyzing hyper-spectral image data and information via dynamic database updating.
Hyper-spectral imaging and analysis has been established as a highly unique, specialized, and sophisticated, combined spectroscopy and imaging type of analytical method or technique, in the more encompassing field or area of analytical science and technology, involving the sciences and technologies of spectroscopy and imaging. By definition, hyper-spectral imaging and analysis is based on a combination of spectroscopy and imaging theories, principles, and practices, which are exploitable for analyzing objects, such as objects in a sample of matter, in a highly unique, specialized, and sophisticated, manner.
Hyper-spectral imaging, in general, generating and collecting hyper-spectral images, and, processing and analyzing hyper-spectral image data and information, in particular, theory, principles, and practices thereof, and, related and associated applications and subjects thereof, such as the more general subject of spectral imaging, are well known and taught about in the prior art and currently practiced in a wide variety of numerous different fields and areas of science and technology. Several (mostly recent) examples of such prior art teachings and practices are disclosed in references 1-19 (and references cited therein). Selected teachings and practices by the same applicant/assignee of the present invention are disclosed in references 20-25 (and references cited therein). For assisting in establishing the field, scope, and meaning, of the present invention, and in understanding problems solved by the present invention, the following background is provided.
Hyper-Spectral Imaging and Analysis
The more highly specialized, complex, and sophisticated, spectroscopic imaging technique of ‘hyper-spectral’ imaging and analysis, in contrast to the regular or standard spectroscopic imaging technique of ‘spectral’ imaging and analysis, consists of using a hyper-spectral imaging and analysis system for on-line (real time, near-real time) or off-line generating and collecting (acquiring) hyper-spectral images and spectra (herein, together, generally referred to as hyper-spectral image data and information), and, processing and analyzing the acquired hyper-spectral image data and information. In hyper-spectral imaging, a sample of matter (containing objects, and components thereof) is exposed to electromagnetic radiation, followed by generation and collection of relatively large numbers of multiple spectral (i.e., hyper-spectral) images, ‘one-at-a-time’, but, in an extremely fast or rapid sequential manner, of the objects (and components thereof) emitting electromagnetic radiation at a plurality of many wavelengths and frequencies, where the wavelengths and frequencies are associated with different selected (relatively narrow) portions or bands, or bands therein, of an entire hyper-spectrum emitted by the objects (and components thereof). A hyper-spectral imaging and analysis system can be operated in an extremely fast or rapid manner for providing exceptionally highly resolved spectral and spatial data and information of an imaged sample of matter, with high accuracy and high precision (reproducibility), which are unattainable by using a regular or standard spectral imaging and analysis system.
Hyper-spectral images generated by, and collected from, a sample of matter, are correlated with emission spectra of the sample of matter, where the emission spectra correspond to spectral representations in the form of spectral ‘fingerprint’ or ‘signature’ pattern types of identification and characterization, of the hyper-spectrally imaged objects (and components thereof) in the sample of matter. Such hyper-spectral image data and information are processed and analyzed by using automatic pattern recognition (APR) or/and optical character recognition (OCR) types of hyper-spectral imaging data and information processing and analysis, for identifying, characterizing, or/and classifying, the physical, chemical, or/and biological, properties, characteristics, and behavior, of the hyper-spectrally imaged objects (and components thereof) in the sample of matter.
Object
Herein, in the context of the field and art of the present invention, the term ‘object’ generally refers to, and is considered synonymous with, at least part of an entity, material, substance, or structure, which, singly or in combination with other objects (entities, materials, substances, or structures), typically as part of a scene (defined hereinbelow), is subjected to a hyper-spectral imaging process or technique. In general, such an object is definable and characterizable by a set of a wide variety of numerous possible biological, chemical, or/and physical, properties, characteristics, and behavior.
Hyper-Spectral Imaging—Generating and Collecting the Hyper-Spectral Image Data and Information
Herein, in the context of the field and art of the present invention, in hyper-spectral imaging, an object (as defined hereinabove) or objects, typically as part of a scene, is/are exposed to natural or/and man-made electromagnetic radiation, followed by generation and collection of multiple spectral (i.e., hyper-spectral) images, via a single field of view, or via a plurality of fields of view, of the object(s) emitting electromagnetic radiation having wavelengths and frequencies associated with different selected (relatively narrow) portions or bands, or bands therein, of an entire spectrum emitted by the object(s). Hyper-spectral images generated by, and collected from, a sample of matter, are correlated with emission spectra of the sample of matter, where the emission spectra correspond to spectral representations in the form of spectral ‘fingerprint’ or ‘signature’ pattern types of identification and characterization, of the hyper-spectrally imaged objects (and components thereof) in the sample of matter.
Imaged Scene of Objects in Hyper-Spectral Imaging
Typically, one performs hyper-spectral imaging of an object or of objects, as part of a scene, in order to ultimately obtain micro scale or/and macro scale (qualitative or/and quantitative) biological, chemical, or/and physical, characteristics, properties, and behavior, of the imaged object(s) which are readily interpretable, understandable, and further usable, by a human operator (observer, viewer, analyzer, or/and controller) of a process involving the imaged object(s) in the scene. Herein, a scene generally refers to surroundings or a place of (i.e., including or containing) a single object, or, a plurality, collection, or ensemble, of several objects (i.e., entities, materials, substances, or structures), wherein takes place or occurs an action or event involving one or more object(s). Accordingly, in the context of the field and art of the present invention, in hyper-spectral imaging, an imaged scene generally corresponds to one or more hyper-spectral images, associated with one or more fields of view, of surroundings or a place of (i.e., including or containing) a single object, or, a plurality, collection, or ensemble, of several objects (i.e., entities, materials, substances, or structures), wherein takes place or occurs an action or event involving one or more imaged object(s). Moreover, in hyper-spectral imaging, an imaged scene includes or contains hyper-spectral image data and information relating to the imaged object(s), particularly in the form of spectral representations, such as spectral fingerprint or signature pattern types of identification and characterization, of the imaged object(s).
Types, Categories, or Classes, of Objects in Hyper-Spectral Imaged Scenes
In general, a scene can be considered as including or containing any number of objects which can be typed, categorized, or classified, according to two main different types, categories, or classes, of objects. Namely, objects of non-interest, and objects of interest, each of which is basically defined as follows. Objects of non-interest correspond to objects of (included or contained in) a scene which are not of interest to a human operator (observer, viewer, analyzer, or/and controller) of a process involving the objects. Objects of interest correspond to objects of (included or contained in) a scene which are of interest to a human operator of a process involving the objects. For further understanding the significantly different meanings and attributes of objects of non-interest and objects of interest, in the context of the field and art of the present invention, objects of non-interest may be considered as being ‘background’ of, or within, a scene, whereas objects of interest may be considered as being ‘targets’ of, or within, a scene.
Accordingly, in hyper-spectral imaging, individual objects among a plurality, collection, or ensemble, of several objects (i.e., entities, materials, substances, or structures) of (included or contained in) the surroundings or place of a scene which is imaged in one or more hyper-spectral images, via one or more fields of view, can be typed, categorized, or classified, according to the above stated two main different types, categories, or classes, of objects, i.e., objects of non-interest (i.e., background), and objects of interest (i.e., targets).
Each main different general type, category, or class, of objects of (included or contained in) a scene is definable or characterizable by one or more sets of a priori or pre-determined known data, information, and parameters, (e.g., in the form of databases of theoretically or/and empirically determined data, information, and parameters) and rules for using thereof, which are obtained and established by a human operator of a process involving the objects of (included or contained in) the scene. For example, such sets of a priori or pre-determined known data, information, and parameters, are typically based on databases of theoretically or/and empirically determined ‘hyper-spectral’ data, information, and parameters, and, on databases of theoretically or/and empirically determined ‘biological, chemical, or/and physical’ data, information, and parameters, which are associatable and correlatable with the objects of the scene, and which are applicable for uniquely identifying (recognizing), discriminating, comparing, filtering, sorting, quantifying, characterizing, and classifying, the objects of the scene.
Processing and Analyzing Hyper-Spectral Image Data and Information
Typically, specific (relatively narrow) portions or bands, or bands therein, of wavelengths and wavelength ranges of the electromagnetic radiation emitted by the object(s) are empirically determined, and then exploited during hyper-spectral imaging, for generating and collecting hyper-spectral images which contain a plethora of hyper-spectral image data and information relating to the imaged object(s), particularly in the form of spectral representations, such as spectral fingerprint or signature pattern types of identification and characterization, of the imaged object(s), that need to be processed and analyzed.
In general, one may consider processing, and analyzing, of hyper-spectral image data and information as two separate, but integrated, main activities, as follows. One may consider processing of hyper-spectral image data and information as being based on, and involving, real time (i.e., in-line or on-line) or/and non-real time (i.e., off-line) automatic (i.e., computerized) data and information manipulating, handling, or/and moving, types of procedures or/and operations. One may consider analyzing of hyper-spectral image data and information as being based on, and involving, real time (i.e., in-line or on-line) or/and non-real time (i.e., off-line) automatic (i.e., computerized) data and information analyzing, identifying (recognizing), discriminating, comparing, filtering, sorting, quantifying, characterizing, and classifying, types of procedures or/and operations. Together, in an integrated manner, real time or/and non-real time processing and analyzing of the hyper-spectral image data and information are performed for the main goal of relating and translating the hyper-spectral image data and information of the imaged object(s) to micro scale or/and macro scale (qualitative or/and quantitative) biological, chemical, or/and physical, characteristics, properties, and behavior, of the imaged object(s) which are readily interpretable, understandable, and further usable, by a human (observer, viewer, analyzer, or/and controller, herein, generally referred to as operator) of a process involving the imaged object(s).
Accuracy, Precision (Reproducibility), Sensitivity, and Speed (Time Scale), of Hyper-Spectral Imaging
Based on the preceding discussion, a scene which is imaged in one or more hyper-spectral images, via one or more fields of view, includes or contains a plurality, collection, or ensemble, of several objects (i.e., entities, materials, substances, or structures), wherein, there exists a number of objects which are objects of non-interest (background), and objects of interest (targets). The plethora of hyper-spectral image data and information represented by, and contained in, hyper-spectral images, via one or more fields of view, of a scene of (including or containing) objects must be processed and analyzed, in particular, by using various combinations of known sets or databases of (theoretically or/and empirically determined) ‘hyper-spectral’ and ‘biological, chemical, or/and physical’ data, information, and parameters, and rules for using thereof, for uniquely identifying (recognizing), discriminating, quantifying, characterizing, and classifying, each object of the imaged scene as being an object of non-interest (background), or as being an object of interest (a target). Only as a result of the integrated processing and analyzing of the hyper-spectral image data and information of the imaged scene of objects, can the hyper-spectral image data and information of the objects be related and translated to micro scale or/and macro scale (qualitative or/and quantitative) biological, chemical, or/and physical, characteristics, properties, and behavior, of the objects which are readily interpretable, understandable, and further usable, by a human operator of a process involving the objects of the imaged scene.
Many, if not most, hyper-spectral imaging and analysis applications involve automatically generating and collecting a relatively large number (e.g., on the order of hundreds, thousands, or millions) of individual hyper-spectral images (each containing a plurality of emission spectra, spectral fingerprints, and spectral patterns), typically, via a plurality of different fields of view, of a plurality of scenes, wherein each scene includes or contains a single object, or a plurality, collection, or ensemble, of several objects (i.e., entities, materials, substances, or structures). Accordingly, such hyper-spectral imaging and analysis applications necessarily involve processing and analyzing ‘huge’ amounts of ‘raw’ hyper-spectral image data and information.
Performance of a given hyper-spectral imaging and analysis application is based on, and influenced by, accuracy, precision (i.e., reproducibility), and sensitivity, of several parameters, and particularly of the main parameter of (spectral and spatial) resolution. Spectral resolution relates to the resolution of the optically detected electromagnetic radiation of the affected energy or emission beam emitted by, and emerging from, illuminated objects of (included or contained in) a scene, from which are generated optical forms, and electronic forms, of hyper-spectral images of the illuminated objects. Spatial resolution relates to the resolution of the topological, morphological or geometrical spaces or/and dimensions within or/and between the chemical, biological, or/and physical, components or elements which comprise a given object (entity, material, substance, or structure) of (included or contained in) an imaged scene. Speed (time scale) relates to the speed (time scale) at which part of a process, or an entire process, of the hyper-spectral imaging and analysis application is performed.
Ordinarily, a human operator of a given hyper-spectral imaging and analysis application inherently desires highly accurate, highly precise (i.e., reproducible), and highly sensitive, generation and collection of hyper-spectral images, as well as highly accurate, highly precise, and highly sensitive, processing and analyzing of the generated and collected ‘raw’ hyper-spectral image data and information. In addition to, and related to, high accuracy, high precision, and high sensitivity, of generating and collecting hyper-spectral images, and of processing and analyzing the ‘raw’ hyper-spectral image data and information therefrom, there is the speed (or time scale) at which these activities and procedures are performed. Ideally, a human operator of a given hyper-spectral imaging and analysis application inherently desires that generating and collecting hyper-spectral images, and processing and analyzing the ‘raw’ hyper-spectral image data and information therefrom, be performed as highly accurately, as highly precise (reproducible), and as highly sensitive, as technically possible or feasible, at as high a speed (or short time scale) as technically possible or feasible. Clearly, actual levels of accuracy, precision, sensitivity, and speed (time scale), of a given hyper-spectral imaging and analysis application are measured, evaluated, compared, and analyzed, relative to known or established criteria and levels of accuracy, precision, sensitivity, and speed (time scale).
As with most activities or/and phenomena, in hyper-spectral imaging and analysis applications, accuracy, precision, sensitivity, and speed (time scale), are often, and usually, not directly related and proportional to each other. In other words, it is often, and usually, difficult to have a hyper-spectral imaging and analysis application which can be characterized at the same time as being highly accurate, highly precise (reproducible), highly sensitive, and of high speed (short time scale). Often, and usually, high accuracy is achieved at the expense of achieving high precision or/and at the expense of achieving high sensitivity or/and at the expense of achieving high speed (short time scale). Similarly, high speed (short time scale) is often, and usually, achieved at the expense of achieving high accuracy or/and at the expense of achieving high precision or/and at the expense of achieving high sensitivity. In practice, any given hyper-spectral imaging and analysis application is characterized by some combination of variable levels of accuracy, precision, sensitivity, and speed (time scale).
One may consider a given hyper-spectral imaging and analysis application as being comprised of two separate, but integrated, domains or stages of main activities or procedures, as follows. The first domain or stage of main activities or procedures is based on, and involves, generating and collecting of the hyper-spectral images. The second domain or stage of main activities or procedures is based on, and involves, processing and analyzing the generated and collected hyper-spectral image data and information. In general, each of these two domains or stages of a hyper-spectral imaging and analysis application can be characterized by various different levels of accuracy, precision (reproducibility), sensitivity, and speed.
Accuracy, precision (reproducibility), sensitivity, and speed (time scale), of the first domain or stage of a hyper-spectral imaging and analysis application are primarily (i.e., not exclusively) determinable and controllable by the types, kinds, quantity, and quality, of ‘physical’ hardware equipment and instrumentation which comprise a given hyper-spectral imaging system, device, or apparatus. Accuracy, precision (reproducibility), sensitivity, and speed (time scale), of the second domain or stage of a hyper-spectral imaging and analysis application are primarily (i.e., not exclusively) determinable and controllable by the types, kinds, quantity, and quality, of (computer) ‘software’ which is used for implementing and operating a given hyper-spectral imaging system, device, or apparatus. Such software includes operatively connected and functioning written or printed data, in the form of software programs, software routines, software sub-routines, software symbolic languages, software code, software instructions or protocols, software algorithms, or/and combinations thereof. Clearly, in essentially all hyper-spectral imaging and analysis applications the just described two domains or stages of main activities or procedures are fully integrated, therefore, in theory, and in practice, accuracy, precision (reproducibility), sensitivity, or/and speed (time scale), of the first domain or stage affects, and is affected by, accuracy, precision (reproducibility), sensitivity, or/and speed (time scale), of the second domain or stage, and vice versa.
The scope of application of the present invention is directed to, and focused on, the preceding stated second domain or stage of main activities or procedures of a hyper-spectral imaging and analysis application, i.e., being based on, and involving, processing and analyzing generated and collected hyper-spectral image data and information. More specifically, wherein the processing and analyzing of hyper-spectral image data and information are based on, and involve, an integrated combination of: (i) real time or/and non-real time automatic (i.e., computerized) data and information manipulating, handling, or/and moving, types of procedures or/and operations, and (ii) real time or/and non-real time automatic (i.e., computerized) data and information analyzing, identifying (recognizing), discriminating, comparing, filtering, sorting, quantifying, characterizing, and classifying, types of procedures or/and operations.
Significant On-Going Problems and Limitations of Processing and Analyzing Hyper-Spectral Image Data and Information
As stated hereinabove, prior art includes a plethora of teachings [e.g., 1-25] of hyper-spectral imaging, in general, generating and collecting hyper-spectral images, and, processing and analyzing hyper-spectral image data and information, in particular. However, significant on-going problems and limitations of processing and analyzing hyper-spectral image data and information are usually based on, involve, or/and are associated with, the theoretical or/and practical difficulties and complexities that arise when performing, or attempting to perform, the varied and numerous data and information processing and analyzing procedures or/and operations with some combination of exceptionally high accuracy, ‘or/and’ high precision (reproducibility), ‘or/and’ high sensitivity, ‘or/and’ at high speed (short time scale), be it during real time or during non-real time, in an optimum or highly efficient manner. Exceptional difficulties and complexities arise when performing, or attempting to perform, the varied and numerous data and information processing and analyzing procedures or/and operations with the ‘ultimate’ combination of exceptionally high accuracy, ‘and’ high precision (reproducibility), ‘and’ high sensitivity, ‘and’ at high speed (short time scale), all at the same time (i.e., simultaneously), be it during real time or during non-real time, in an optimum or highly efficient manner.
There exists a wide variety of numerous different exemplary specific cases of hyper-spectral imaging and analysis applications wherein theoretical or/and practical difficulties and complexities arise when performing, or attempting to perform, the varied and numerous data and information processing and analyzing procedures or/and operations with some combination of exceptionally high accuracy, or/and high precision (reproducibility), or/and high sensitivity, or/and at high speed (short time scale), or, all at the same time (i.e., simultaneously), be it during real time or during non-real time, in an optimum or highly efficient manner. For background purposes, only a few such exemplary specific cases of hyper-spectral imaging and analysis applications are described herein, as follows.
As described hereinabove, a scene which is imaged in one or more hyper-spectral images, via one or more fields of view, includes or contains a plurality, collection, or ensemble, of several objects (i.e., entities, materials, substances, or structures), wherein, there exists a number of objects which are objects of non-interest (background), or/and objects of interest (targets). Typically, each hyper-spectrally imaged scene of a sample of matter includes or contains a distribution of different relative numbers (i.e., ratios, proportions) of the preceding defined two main different types, categories, or classes, of objects. For example, a given hyper-spectrally imaged scene may include or contain a distribution of a relatively small number of objects of interest (targets), and a relatively large number of objects of non-interest (corresponding to a relatively high or ‘noisy’ background). Conversely, a given imaged scene may include or contain a distribution of a relatively large number of objects of interest (targets), and a relatively small number of objects of non-interest (corresponding to a relatively low or ‘quiet’ background).
Moreover, for example, there are many hyper-spectral imaging and analysis applications wherein the majority of hyper-spectrally imaged scenes include or contain a relatively ‘exceptionally’ small number of objects of interest (targets) compared to a relatively large number of objects of non-interest (high or noisy background). For example, such applications are wherein the number of objects of interest (targets), relative to the number of all objects [of interest (target) and of non-interest (background)] of (included or contained in) a hyper-spectrally imaged scene, corresponds to a ratio or proportion as low as 1% [1 part per hundred (pph)], or 10−1% [1 part per thousand (ppt)], or 10−4% [1 part per million (ppm)], 10−7% [1 part per billion (ppb)], or even as low as 10−10% [1 part per trillion (pptr)].
In addition to hyper-spectrally imaged scenes including or containing distributions of different relative numbers (ratios, proportions) of the two main different types, categories, or classes, of objects, it is noted that each hyper-spectrally imaged object (i.e., entity, material, substance) is definable and characterizable by a set of a wide variety of numerous possible biological, chemical, or/and physical, properties, characteristics, and behavior. For example, in a given hyper-spectrally imaged scene, there may exist different relative numbers, and types kinds, of objects whose ‘hyper-spectral’ image data and information (particularly including, for example, emission spectra corresponding to spectral representations in the form of spectral fingerprint or signature pattern types of identification and characterization), are quite similar, or even nearly identical, i.e., barely distinguishable or resolvable, but whose ‘biological, chemical, or/and physical’ data and information (in terms of properties, characteristics, or/and behavior), are significantly different, and not at all similar or nearly identical, i.e., easily distinguishable or resolvable, or vice versa.
Regardless of the actual distributions of the different relative numbers (i.e., ratios, proportions) of objects of interest (targets) and objects of non-interest (background) in hyper-spectrally imaged scenes of a sample of matter, any hyper-spectral imaging and analysis application ultimately involves the need for identifying, distinguishing, and resolving, the objects of interest (targets) from the objects of non-interest (background) in the hyper-spectrally imaged scenes. This involves the need for identifying, distinguishing, and resolving, the hyper-spectral image data and information of the objects of interest (targets) from the hyper-spectral image data and information of the objects of non-interest (background). Moreover, there is also the need for performing such identifying, distinguishing, and resolving, procedures and operations in relation to the biological, chemical, or/and physical data and information of the objects of interest (targets) and of the objects of non-interest (background), in the hyper-spectrally imaged scenes.
In hyper-spectral imaging, processing and analyzing hyper-spectral image data and information is performed according to various different speeds or time scales. For example, there are many hyper-spectral imaging and analysis applications which, by definition, and in accordance with the particular characteristics, needs, or requirements, of such applications, necessarily require that processing and analyzing hyper-spectral image data and information be performed at exceptionally high speeds, for example, on the order of thousands or millions of data or/and information items per second, or, equivalently, at exceptionally short time scales, for example, on the order of milliseconds (msec) or microseconds (μsec) per data or/and information operation. This is particularly the case for hyper-spectral imaging and analysis applications which involve automatically generating and collecting relatively large numbers (e.g., on the order of tens or hundreds of thousands, or even millions) of individual hyper-spectral images (each containing a plurality of emission spectra, spectral fingerprints, and spectral patterns), typically, via a relatively large number of different fields of view, of a relatively large number of scenes, wherein each scene includes or contains a single object, or a plurality, collection, or ensemble, of several objects (i.e., entities, materials, substances, or structures).
For each of the preceding briefly described exemplary specific cases of hyper-spectral imaging and analysis applications, various theoretical or/and practical difficulties and complexities typically arise when performing, or attempting to perform, the varied and numerous data and information processing and analyzing procedures or/and operations with some combination of exceptionally high accuracy, or/and high precision (reproducibility), or/and high sensitivity, or/and at high speed (short time scale), be it during real time (i.e., in-line or on-line) or during non-real time (i.e., off-line), in an optimum or highly efficient manner. Thus, although prior art includes a plethora of teachings of hyper-spectral imaging, in general, generating and collecting hyper-spectral images, and, processing and analyzing hyper-spectral image data and information, in particular, there clearly exists an on-going need for overcoming the various and numerous significant on-going problems and limitations of processing and analyzing hyper-spectral image data and information.
There is thus a need for, and it would be highly advantageous to have a method of processing and analyzing hyper-spectral image data and information via dynamic database updating.
There is a need for such a method which, during real time (i.e., in-line or on-line) or/and during non-real time (i.e., off-line), optimally and highly efficiently, integrates the two main activities of processing, and analyzing, hyper-spectral image data and information, namely, (i) automatic (i.e., computerized) data and information manipulating, handling, or/and moving, types of procedures or/and operations, and, (ii) automatic (i.e., computerized) data and information analyzing, identifying (recognizing), discriminating, comparing, filtering, sorting, quantifying, characterizing, and classifying, types of procedures or/and operations. Moreover, there is a need for such a method which integrates the varied and numerous hyper-spectral image data and information processing and analyzing procedures or/and operations with the ‘ultimate’ combination of exceptionally high accuracy, ‘and’ high precision (reproducibility), ‘and’ high sensitivity, ‘and’ at high speed (short time scale), all at the same time (i.e., simultaneously), be it during real time or during non-real time, in an optimum or highly efficient manner.
Additionally, there is a need for such a method which is implementable for achieving the main goal of relating and translating the hyper-spectral image data and information of imaged objects to micro scale or/and macro scale (qualitative or/and quantitative) biological, chemical, or/and physical, characteristics, properties, and behavior, of the imaged objects which are readily interpretable, understandable, and further usable, by a human operator (observer, viewer, analyzer, or/and controller) of a process involving the imaged objects.
There is a further need for such a method which is generally applicable to essentially any type, kind, or number, of objects (entities, materials, substances, or structures), as part of a scene, which are subjected to a hyper-spectral imaging process or technique. Moreover, wherein the objects are definable and characterizable by a set of a wide variety of numerous possible biological, chemical, or/and physical, properties, characteristics, and behavior.
There is a further need for such a method which is generally applicable to processing and analyzing hyper-spectral image data and information of hyper-spectral images of objects which are generated and collected from the objects emitting electromagnetic radiation having wavelengths and frequencies associated with different portions or bands, or bands therein, of an entire spectrum emitted by the objects, such as the ultra-violet (UV) band, the visible (VIS) band, the infra-red (IR) band, and the deep infra-red band.
There is a further need for such a method which is generally applicable to processing and analyzing hyper-spectral image data and information of hyper-spectral imaging and analysis applications involving automatically generating and collecting relatively large numbers (e.g., on the order of hundreds, thousands, or millions) of individual hyper-spectral images (each containing a plurality of emission spectra, spectral fingerprints, and spectral patterns), typically, via a plurality of different fields of view, of a plurality of scenes, wherein each scene includes or contains a single object, or a plurality, collection, or ensemble, of several objects (i.e., entities, materials, substances, or structures).
There is a further need for such a method which is generally applicable to processing and analyzing hyper-spectral image data and information of hyper-spectral imaging and analysis applications wherein the majority of imaged scenes include or contain an exceptionally relatively small number of objects of interest (targets) compared to a relatively large number of objects of non-interest (high or noisy background). For example, such cases wherein the fraction or concentration of the objects of interest (targets), relative to all objects [of non-interest (background) and of interest (targets)] of (included or contained in) an imaged scene, corresponds to as low as 1% [1 part per hundred (pph)], or 10−1% [1 part per thousand (ppt)], or 10−4% [1 part per million (ppm)], 10−7% [1 part per billion (ppb)], or even as low as 10−10% [1 part per trillion (pptr)].
There is a further need for such a method which is generally applicable to processing and analyzing hyper-spectral image data and information of hyper-spectral imaging and analysis applications which require distinguishing or resolving quite similar, or even nearly identical, hyper-spectral image data, information, and parameters, in relation to significantly different biological, chemical, or/and physical data, information, and parameters, of objects in imaged scenes.
There is a further need for such a method which is generally applicable to processing and analyzing hyper-spectral image data and information of hyper-spectral imaging and analysis applications according to various different speeds or time scales. For example, wherein such hyper-spectral imaging and analysis applications which, by definition, and in accordance with the particular characteristics, needs, or requirements, necessarily require that processing and analyzing hyper-spectral image data and information be performed at exceptionally high speeds, for example, on the order of thousands or millions of data or/and information items per second, or, equivalently, at exceptionally short time scales, for example, on the order of milliseconds (msec) or microseconds (μsec) per data or/and information operation.
There is a further need for such a method which is generally applicable to processing and analyzing hyper-spectral image data and information of hyper-spectral images of objects which are generated and collected by using various different types or kinds of hyper-spectral imaging systems, devices, or/and apparatuses, which are operable during real time (i.e., in-line or on-line) or/and during non-real time (i.e., off-line). Accordingly, there is a further need for such a method which is generally applicable to, and integratable with, various different types or kinds of physical hardware equipment and instrumentation, and, (computer) software, which comprise a given hyper-spectral imaging system, device, or apparatus, which is operable during real time (i.e., in-line or on-line) or/and during non-real time (i.e., off-line).
There is a further need for such a method which is commercially applicable in a wide variety of different fields and areas of technology, and associated applications thereof, which either are, or may be, based on, involve, or benefit from the use of, hyper-spectral imaging, in general, generating and collecting hyper-spectral images, and, processing and analyzing hyper-spectral image data and information, in particular.